System and process for analyzing a medical condition of a user

ABSTRACT

A device and a process for analyzing a medical condition of a user. This device and process can also be used to predict a future abnormal medical condition of the user. The device includes a portable information-receiving device, an information processing device and a remote storage and processing device. These three devices may be in communication with each other via a wireless communication system. This device can include a GPS system for locating the user when the user is having an abnormal medical condition. The process is designed to take a digital signal from a plurality of ECG sensors on the portable information device and form a QRS wave. One or more points are extracted from this QRS wave are used to form a QRS complex wave. QRS complex waves are used to analyze the medical condition of the user. A plurality of parameters are calculated from these points on the QRS wave. If the medical condition of the user is in an abnormal range then an alarm will sound. However, if the user is not in an abnormal range, then the device may also predict the possibility of a future occurrence of an abnormal medical condition in the user.

CROSS REFERENCE TO RELATED APPLICATIONS

This patent application is based upon Provisional Patent ApplicationSer. No. 60/295,194 filed on Jun. 1, 2001 and a second provisionalPatent Application, Ser. No. 60/339,875, filed on Oct. 31, 2001 whereinpriority is claimed under 35 U.S.C. § 119e.

BACKGROUND

Portable medical information analyzers are known in the art. Forexample, medical information analyzers are disclosed in the followingU.S. Pat. Nos. 6,206,829; 6,171,264; 6,171,237; 6,162,180; 6,160,478;6,149,585; 6,108,578; 6,102,856; 6,100,806; 6,093,146; 6,072,396;6,067,466; 6,049,794; 6,047,203; 6,039,688; 6,011,989; 5,971,921;5,959,529; 5,942,986; 5,941,829; 5,931,791; 5,921,938; 5,919,141;5,878,746; 5,876,351; 5,873,369; 5,855,550; 5,840,020; 5,782,878;5,772,586; 5,735,285; 5,704,364; 5,678,562; 5,564,429; 5,544,661;4,909,260, incorporated herein by reference.

In addition, the following references are also known in the art: RussianApplication No. 98106704/13 M.C. A615b5/02, A61B5/0452 published on Oct.2, 2000. In addition Russian application No. 98103717/14 M. cl.A61B5/0452 published on Oct. 1, 2000; Russian Patent Application No.97113351/14 M. cl. A61B5/02 published on Feb. 6, 1999; and finally,Russian Patent Application No. 93016579.

All of these references have one or more significant disadvantages.First, there are few parameters being analyzed in that only oneparameter may be analyzed at one time instead of multiple parameters.Second, there is no real time complex approach to a patient's complexpresent state evaluation because one or more of the above referencesrequire the direct participation of an expert evaluating the parameters.Third, many of the devices described by the prior art require thepatient to be located adjacent to the evaluation device. Sinceevaluation devices may be the size of a personal computer, this limitsthe availability for patients to use these monitoring devices.

In addition, in all of the previous models, the ECG waves were readdirectly and then analyzed. In the present invention a few preliminarypoints are analyzed first and then the ECG information is reconstructedin the form of a QRS wave. In addition, this invention involves apredictive model that uses one or more parameters derived from this QRSwave to determine the possibility of the user experiencing an abnormalmedical occurrence.

Thus, in the past, patients may have been reluctant to use these medicalinformation analyzers because they were too large and cumbersome.Furthermore, doctors or other medical professionals may have beenreluctant to prescribe the use of these portable medical informationanalyzers because they might provide insufficient, or incorrect medicalinformation.

For example, this medical information analyzer could be used to detectheart arrhythmias. Arrhythmias are a disturbance in the rate or rhythmof the heartbeat. Various arrhythmias can be symptoms of serious heartdisorders; however, they are usually of no medical significance exceptin the presence of additional symptoms.

The heart's rhythm is controlled by an electrical impulse that isgenerated from a clump of tissue on the right atrium called thesinoatrial node, often referred to as the heart's natural pacemaker. Ittravels to a second clump of tissue called the atrioventricular node andthen to the ventricles. Bradycardia, or slow heartbeat, is often presentin athletes. It may, however, indicate conduction problems, especiallyin older people. In one type of bradycardia, called sinoatrial oratrioventricular block, or heart block, rhythm can be maintained byimplanted electrodes that act as artificial pacemakers. Drugs, caffeine,anemia, shock, and emotional upset can precipitate tachycardia orheartbeat faster than 100 beats per minute in the adult. It may also bea sign of over activity of the thyroid gland or underlying disease.

Flutters, and the even faster fibrillations, are rapid, uncoordinatedcontractions of the atrial or ventricular muscles that usually accompanyheart disorders. Atrial fibrillation may be idiopathic, the result ofrheumatic mitral valve disease (see rheumatic fever) in young people orhypertensive heart disease (see hypertension) and arteriosclerotic heartdiseases (see arteriosclerosis) in older people. It may result in arapid pulse rate and may be associated with thrombus formation in theatria and a risk of embolization to the brain (stroke) or other organs.

Atrial fibrillation is often treated with digitalis. Ventricularfibrillation is a sign of the terminal stage of heart failure and isusually fatal unless defibrillation is achieved by immediatedirect-current defibrillation. Some tachycardias can be managed by theimplantation in the upper chest of small defibrillators that sensedangerous fibrillations and administer an electric shock to the heart torestore normal rhythm. The Columbia Encyclopedia, Sixth Edition. 2001.

SUMMARY OF THE INVENTION

These problems are overcome by providing a new system and process forevaluating a medical condition of a patient by providing an improvedportable medical information analyzer and an improved centralinformation-processing device in one system.

One object of this invention is to provide a process for calculating aQRS line based upon a limited number of preliminary points or indiciataken from an individual.

Another object of the invention is to provide an accurate estimator ofthe future probability of an abnormal medical condition by using apatient's ECG reading in a customized algorithm to determine thepatient's risk for incurring an abnormal medical condition.

Still another object of the invention is to provide a portableinformation device that is designed to receive signals from a userwherein these signals are then analyzed as described in the first twoobjects of the invention.

The invention relates to a portable medical information-analyzing devicethat may comprise at least one sensor for extracting medical informationfrom a user. The user could either be a person or an animal.

This device also comprises at least one transceiver for transmittingthis medical information from the user and at least one externalinterface to control external equipment that relates to the user. Inaddition, the transceiver is designed to receive information from theinformation-processing device. Thus, this portable information devicesends this information onto an information-processing device andreceives information from it. The information-processing devicecomprises at least one transceiver for receiving and transmitting themedical information from and to the portable information device.

There is also at least one medical information analyzer, whichcalculates a series of medical-based parameters from this medicalinformation. The system also includes at least one data store incommunication with the medical information analyzer. The data storestores a set of predetermined data on these medical-based parameters.There is also at least one parameter analyzer, which compares a set ofpredetermined data in the data store with the medical-based parameterscalculated from this medical information.

Once this information has been determined, it is sent to an abnormalityidentifier, which then determines whether these parameters are out ofline with the present set of parameters, and determines a riskassessment, which is used to determine the type of alarm used to signalthe user. Finally, there is also an alarm, which sends an alarm signalto the user when the medical based parameter analyzer determines amedical abnormality based upon a comparison of the set of predetermineddata with the medical information.

The invention also relates to a process for analyzing the medicalcondition of the user. In this case, the process can either operateusing the device described above or use another type device such as apersonal computer. The process includes the first step of gatheringmedical information from a user. Next, the process involves sending thisinformation to a medical information analyzer. The medical informationanalyzer extracts particular points on a QRS wave out of the ECGinformation, analyzes this information, and then reconstructs the QRSwave so that it does not have any noise. Next, this analyzer breaks thisinformation down into a plurality of discrete parameters. Theseparameters are: 1) pulse rate; 2) immediate alteration of pulse rate; 3)R—R interval; 4) premature beats; 5) group of premature beats; 6) atrialfibrillation flutter; 7) ST-segment depression/elevation; 8) T-waveinversion; 9) width of Q-wave; 10) Ratio of Amplitude of Q-wave toAmplitude of R-wave; 11) Amplitude of R wave; 12) Width of QT-interval;13) Width of QRS complex; 14) Width of PQ interval; and 15) StandardDeviation of the average normal-to-normal R—R intervals. Theseparameters are then compared with a set of pre-set parameters todetermine whether a user is experiencing abnormal symptoms. Dependingupon the value of the calculated parameter, the parameter definition,and the risk analysis, an alarm may be activated to signal the user thatthe user has entered an abnormal medical condition.

To predict whether the user will experience an abnormal medicalcondition, such as the complex risk of sudden cardiac death, a pluralityof parameters are compiled in a formula. These parameters relate to thepreviously mentioned parameters in that these parameters involve the STinit., which is the ST segment level before the observation begins; STmeas., which is the ST segment level at the current movement; STthresh., which is the ST segment threshold at normal levels; QT meas.which is the QT interval duration at the current moment; QT norm., whichis the QT interval normal duration.

Thus, this formula builds upon the parameters that are calculated whendetermining whether the user is experiencing an abnormal medicalcondition.

In addition, this type of risk can be adjusted for each user so that thesystem learns the boundaries for each user. Ultimately, once theabnormal medical condition has been determined, the system determineswhich alarm to activate to warn the user. This information in the formof the alarm is then communicated to the portable information device tocontrol external equipment or stimuli at the user based upon the medicaland environmental information.

BRIEF DESCRIPTION OF THE DRAWINGS

Other objects and features of the present invention will become apparentfrom the following detailed description considered in connection withthe accompanying drawings, which disclose several embodiments of thepresent invention. It should be understood, however, that the drawingsare designed for the purpose of illustration only and not as adefinition of the limits of the invention.

In the drawings wherein similar reference characters denote similarelements throughout the several views:

FIG. 1A is a schematic block diagram of the system for processing andanalyzing information related to the health of a user;

FIG. 1B is a second embodiment of the schematic block diagram in FIG.1A;

FIG. 2 is a schematic block diagram of a more detailed view of aportable information device shown in FIGS. 1A and 1B;

FIG. 3 is a schematic block diagram of a first embodiment of theinformation-processing device shown in FIG. 1A;

FIG. 4 is a schematic block diagram of a second embodiment of theinformation-processing device shown in FIG. 1A;

FIG. 5 is a schematic block diagram of a third embodiment of theinformation-processing device shown in FIG. 1A;

FIG. 6 is a flowchart of a process for receiving and sending informationfrom the portable information device to the information-processingdevice;

FIG. 7A is a flow chart of a process for extracting and calculating aplurality of parameters from the information sent from the portableinformation device;

FIG. 7B is a flow chart of a process for determining a series of pointsalong a QRS complex graph shown in FIG. 8A;

FIG. 8A is a graph relating to the plurality of characteristic pointsand parameters taken from information obtained from the portableinformation device;

FIG. 8B is a graph showing a division of QRS fragment;

FIG. 9A is a graph showing a close up view of a QRS complex showingwhere point R has been preliminarily identified;

FIG. 9B is a graph showing a close up view of a QRS complex showing therefinement of point R;

FIG. 9C is a graph showing a close up view of a QRS complex showingwhere real point R has been defined;

FIG. 10A is a close up view of a QRS complex showing a normal Q wave;

FIG. 10B is a close up view of a QRS complex showing a depressed Q wave;

FIG. 10C is a close up view of a QRS complex showing a Q-wave typicalfor a group of extrasystoles;

FIG. 11A is a close up view of a QRS complex showing a normal S-wave;

FIG. 11B is a close up view of a QRS complex showing a depressed S-wave;

FIG. 11C is a close up view of a QRS complex showing a S-wave typicalfor a group of extrasystoles;

FIG. 12A is a close up view of a QRS complex showing a normal T wave;

FIG. 12B is a close up view of a QRS complex showing an inverse T wave;

FIG. 12C is a close up view of a QRS complex showing a normal T wavewith a weakly expressed maximum;

FIG. 13 is a flow chart showing an analysis of the plurality ofparameters;

FIG. 14 is a table representing the plurality of parameters and theirdefinitions;

FIG. 15 is a flow chart for predicting an occurrence of a health riskusing the table of parameters;

FIG. 16 is a table showing a process to determine whether to trigger analarm/warning regarding the information processed in FIG. 15;

FIG. 17 is a flowchart showing a process for triggering a warning basedupon an inference risk analysis;

FIG. 18 is a flowchart showing the process of three levels of adaptivechanges to parameter threshold conditions; and

FIG. 19 is a flowchart showing the process for locating a user once thealarm has been triggered.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

Referring in detail to the drawings, FIGS. 1A-5 show the differentembodiments of a device for determining an abnormal medical conditionwhile FIGS. 6-19 show the process for determining and predicting anabnormal medical condition. The process may be preferably carried outvia the device but the process according to the invention is notrestricted to this device according to the invention.

FIGS. 1A and 1B are schematic block diagrams of the system 50 forprocessing and analyzing information related to the health of a user.System 50 includes a portable information device 100 a centralinformation processing device 200 and a central information storagedevice 300 all in communication with each other. As shown in FIG. 1Athere is a wireless connection as shown that can include communicationvia Bluetooth™ technology, radio frequency, infrared communication, orany other wireless connection known in the art. FIG. 1B shows abi-directional communication between these devices can be in the form ofa hard-wired connection or through wireless communication. Thehard-wired connection is shown through lines 80 showing the optional useof communication lines 80 such as Ethernet or other type cabling knownin the art.

Essentially, portable information device 100 reads medical informationfrom a user such as a human being or an animal. This information, whichis in the form of packaged digitalized bioelectric signals, is thencommunicated to information processing device 200. Once this informationhas been received it is entered into a FIFO buffer 214 and then a Cbuffer 216 before it is analyzed. Information processing device 200analyzes this information and then determines whether the user isexperiencing any abnormal symptoms. Next, information processing device200 either sends an alarm or control signal to the user, portableinformation device 100, or to a medical professional if the user isexperiencing any abnormal symptoms. Information-processing device 200can also signal to portable information device 100 to cause variousactions to the user, or the users' environment.

FIG. 2 is a schematic block diagram of a more detailed view of aportable information device 100 shown in FIGS. 1A and 1B. Portableinformation device 100 includes a housing 110 which can be in the formof a belt that wraps around the user, a garment, or other housing thatcontains the sensors, or the sensors may be implanted within the body.Housing 110 contains an alarm 115 to alarm the user when the user has anabnormal medical condition. In addition, included with this housing is abattery pack or power supply 118 that powers the components in thehousing and the sensors and stimuli outside the housing. On the outsideface of housing 110 is a plurality of sensors 120 that read medicalinformation from the user in the form of an analog signal. Other remotesensors 122 (See FIG. 1A) are disposed outside housing 110 and maybe inthe form of patches on the user, or may be embedded or implanted. Theseplurality of sensors are in communication with an amplifier 130 or amicrocontroller 150 via a signal processor 134. Amplifier 130 amplifiesthe analog signal received from sensors 120 so that this information canbe read by analog to digital (A/D) converter 140. Analog to digitalconverter 140 converts the analog information into digital information.In addition, signal processor 134 processes signals from sensors 122 sothat these signals can then be sent to microcontroller 150.

A battery level manager 170 is coupled to microcontroller 150 to controlthe energy use in that device. In addition, also coupled to the deviceis a stimuli reader 180, a stimuli generator 182, and a signaldemodulator 184 all coupled to transceiver 160. There is also a GPStracking system 190, which is shown in dashed lines and coupled toprocessor 150. The dashed lines are present because this device can beeither coupled to portable information device 100, or to informationprocessing device 200 as GPS system 290. In addition, an externalcontroller 175 as shown in FIG. 1A can be in communication withtransceiver 160 to instruct stimuli generator (See FIG. 2) to create astimuli action on a user such as an injection of medication or shocktherapy.

Sensors 122 monitor other information concerning the user. Several ofthe sensors may read information concerning the user's environment, suchas temperature, humidity and location (GPS). The information from thesesensors is sent to signal processor 134, which feeds the information tothe microcontroller 150. There is also the capability to receiveinformation from the information-processing device 200. This informationis decoded in signal demodulator 184 and is used to control variousexternal stimuli such as, but not limited to, the dispensing of variouspharmaceuticals, electrical stimulus, auditory and visual stimulus, andother sensory stimulus.

The digital information is then sent to microcontroller 150 which can bein the form of a controller that creates a series of data packets, whichcontains information about the ECG fragments. This information is thensent through transceiver 160 which can either be a wire-basedtransceiver or a wireless transceiver such as a Bluetooth™ transceiveror an infrared transceiver. If transceiver 160 is wire based, then theinformation flows through lines 80 (FIG. 1B) and on to informationprocessing device 200.

The portable information device 100 also receives signals from theinformation-processing device 200. These signals may be commandsassociated with the information being sent from the portable informationdevice or the information may be independent.

FIG. 3 is a schematic block diagram of a first embodiment ofinformation-processing device 200 substantially shown in FIG. 1A.Information processing device 200 can be in the form of a PC, or in theform of a portable device that can attach to a user or may be in otherforms. Information processing device 200 can be powered either by abattery or an A/C power supply (not shown). Information processingdevice 200 includes a bi-directional wireless or wire based transceiver210 that sends and receives information either through a wire basedsystem such as a LAN or a wireless system such as Bluetooth™ technology,infrared or RF frequency transmissions. If information processing device200 is in the form of a personal computer, then the method forcommunication will most likely be wireless. However, if informationprocessing device 200 is in the form of a portable device then themethod for communication could be either wire or wireless based.

With this first embodiment, there are a series of elements all stored ona single programmable integrated circuit or IC 220. Because anintegrated circuit or IC 220 is used, these elements are distinguishedprimarily by the software that is used on the circuit. IC 220 includesone or more medical information analyzers 230. Medical informationanalyzer 230 first extracts a plurality of points taken from ECG waves.Next analyzer 230 reconstructs a QRS wave using these points. Next,medical information analyzer calculates a series of medical-basedparameters from the medical information received by transceiver 210.These medical-based parameters are calculated using a series of formulasbased upon points plotted on a graph shown in FIG. 8A.

In addition, disposed on IC 220 is at least one data store 240, which isin communication with medical information analyzer 230. Data store 240stores a set of predetermined data based on the set of medical-basedparameters. This predetermined data can be adjusted or adapted to changeonce new information enters the system. The adaptive changes can bebased upon a single user, or a population of users. The predetermineddata is a result of a series of tests performed on users that experiencemedical abnormalities such as a myocardial infarction or heart attack.

In addition, on IC 220 is at least one medical-based parameter analyzer250, which compares either the set of predetermined data in data store240 or personalized predetermined data in personal data store 245 withthe medical-based parameters, calculated from the medical information.

Parameter analyzer 250 collects and analyzes data form a history fileand a cardiac events log file. Parameter analyzer 250 determines the newranges of threshold parameters based demographic data, history file, andlog file of cardiac events. Parameter analyzer 250 then presets theprevious baseline parameters based upon the results of the adaptiveanalyzer to personal data store 245.

If parameter analyzer 250 finds data that is consistently in an abnormalrange, then an abnormality identifier 260 sends information to an alarmcontroller 280. Alarm controller 280 sends this information to atransceiver 284 that includes a modem 286 and a telephone line 288 or toan alarm 294. Modem 286 sends these readings along with an analysis ofthese readings either to a doctor or on to a user such as a patient inthe form of an alarm or warning wherein this alarm or warning issignaled on alarm 115.

The alarm controller can also determine that certain actions may berequired at the user. In which case, information concerning the alarmand required action is communicated to the portable information deviceon the patient where external devices may be used to administer medicineor external stimuli.

In addition, there is also an external stimuli analyzer 261 and anenvironmental analyzer 263 also disposed within IC 220. External stimulianalyzer 261 receives information from abnormality identifier 260 anddetermines whether to introduce an external stimuli to the user throughstimuli generator 182 via transceiver 160. In most cases alarmcontroller 280 will simultaneously send an alarm to the user to warn theuser of his or her abnormal medical condition.

Environmental analyzer 263 receives information from portableinformation device 100 regarding the user's environment such astemperature, and humidity. Environmental analyzer will analyze thisinformation to warn the user to seek an alternative environment if thecurrent environment is harmful to the user. Essentially, environmentalanalyzer 263, which is coupled to abnormality identifier 260 and alarmcontroller 280, signals alarm controller 280 to send out an alarm whenthe user's environment has exceeded recommended levels. This signal issent through transceiver 210 to transceiver 160 to warn the user. A GPSsystem 290, which may be optional, can be coupled to informationprocessing device 200. This GPS system 290 is useful if informationprocessing device 290 is positioned on the body of a user or adjacent toa user. In that way, the GPS position identified by GPS system 290,would be the same or substantially similar to the GPS position of theuser.

There is also a display and input output device 296 that connects toinformation processing device 200 that allows a user to see informationthat is processed within information processing device 200. Display 296can be either a standard monitor coupled to a personal computer or acustomized display for a portable version of information processingdevice 200.

Besides receiving information from the portable information device 100,information developed by other forms of cardiac monitoring equipment canalso be read and processed by the system. Information sources may be,but not limited to, Holter monitors, card loop monitors, and othersimilar sources.

In addition to the ECG monitoring and analysis functions, the systemalso analyzes other health and environmental data coming from externalsensors. The data is analyzed in conjunction with outputs of Abnormalityidentifier 260 or analyzed separately in external stimuli analyzer 261.Data is also analyzed in environmental analyzer 263. The environmentalanalyzer 263 compares the environmental data received from the portableinformation device 100 and compares the received conditions to presetupper and lower bounds for each environmental condition. Exceeding theupper and lower bound will generate a signal to the abnormalityidentifier 260. The output of the combined analysis will vary dependingon the individual patient condition. There may be a set of preset levelsagainst which the analysis is conditioned, or the upper and lowerconstraints may be set according to the individual user conditions.

If the stimuli analyzer determines that an external stimuli response isrequired, then the stimuli controller 262 sends a signal to thetransceiver 210. In this step, the stimuli signals are sent to thetransceiver 160, which sends the data to the external controller.

FIG. 4 is a schematic block diagram of a second embodiment of theinformation-processing device shown in FIG. 1. In this embodiment,medical information analyzer 230, medical-based parameter analyzer 250,and abnormality identifier 260, external stimuli analyzer 261, externalstimuli controller 262, environmental analyzer 263, commutator 270 andalarm controller 280 are all disposed on a single integrated circuit(IC) circuit 221. Data store 240, and personal data store 245, arecoupled to (IC) 221 as physically separate elements. However, thesephysically separate elements still work in a manner similar to thecomponents in (IC) 220 to produce substantially the same result.

FIG. 5 is a schematic block diagram of a third embodiment of theinformation-processing device shown in FIG. 1. In this embodiment all ofthe components originally on IC circuit 220, are now separate elementscoupled to each other and in communication with each other. Theseseparate elements, include medical information analyzer 230, data store240, medical parameter analyzer 250, abnormality identifier 260,commutator 270, and alarm controller 280. Not included in this designare external stimuli analyzer 261, stimuli controller 262 andenvironmental analyzer 263. All other elements such as transceiver 284are also connected to these components as separate elements. Inaddition, in this embodiment, GPS system 290 is coupled to portableinformation device 100. With this design, portable information device100 can be located in a remote location, away from informationprocessing device 200 and still deliver the location of the user whenthe user is experiencing an abnormal medical condition.

FIG. 6 is a flow chart of a process for receiving and sendinginformation from portable information device 100 to theinformation-processing device 200. In this process, in step 602, signalsare read from sensors 120. These signals, in the case of ECG signals,which measure electrical impulses from the heart, are the pulse rate andthe heartbeat of the user. Other signals relating to the health andenvironment of the user may also be combined. Next, in step 604, thesesignals are then amplified by amplifier 130. In step 606, these signalsare then converted from analog to digital using analog to digitalconverter 140. This conversion of the signals reduces the bandwidthnecessary for transmitting the signals.

In step 608, these signals are sent through a MICRO controller, whichturns this information into a series of 8 bit ECG bytes, wherein N bytesof information are combined into one packet, which is sent totransceiver 160. Next, in step 610 this information is sent throughtransceiver 160, whereby it is then received in step 612 in totransceiver 210 in information processing device 200. Next, in step 614,this information is archived.

In step 616, this information is then stored into FIFO buffer 214. Thisinformation is then smoothly read out so that the discrete packets ofinformation can now be read out as a continuous flow of information.Next, in step 618, the current value is read into C buffer 216, whichalways contains the latest ECG fragment of a given length. In step 620,this information is then sent for analyzation by feeding thisinformation from C-Buffer 216 to information analyzer 230.

FIG. 7A is a flow chart of a process for extracting and calculating froman ECG reading a plurality of points contained in a QRS wave and thenplotting this wave. In addition, this process includes the calculationof parameters from the information contained in these points. Theseparameters are known in the art and are used to aid a health careprofessional in the evaluation of a patient or user of cardiacequipment. These parameters are: 1) pulse rate; 2) immediate alterationof pulse rate; 3) R—R interval; 4) premature beats; 5) Group ofpremature beats; 6) The atrial fibrillation-flutter; 7) ST-segmentdepression/elevation; 8) T-wave inversion; 9) Width of Q-wave; 10) Ratioof Amplitude Q-wave to amplitude R-wave; 11) Amplitude of R-wave; 12)Width of QT-interval; 13) Width of QRS complex; 14) Width ofPQ-interval; and 15) Standard deviation of the average normal-to-normalR—R-intervals.

For example, in step 704, the information in information analyzer 230 ismanipulated so that a series of R-peak are extracted. R-peaks are pointson the ECG wave or QRS fragment shown in FIG. 8A and in FIGS. 9A, 9B,and 9C, whereby these peaks are the periodic maxima of this wave. Tofind the R-peaks, the information analyzer determines the highestincrement of amplitude A in the wave across a particular period D (FIG.9A-9C).

This determination is essentially made through the following formulas:

(V−V ₁)>A ₁  (1)

OR

(V−V ₂)>A ₂  (2)

Whereby

V—is the amplitude at a current point along the QRS fragment;

V₁ is the amplitude in point (t−d₁);

V₂ is the amplitude in point (t−d₂);

t is the current time;

d₁, d₂, A₁ and A₂ are empiric constants whereby as an example:

d₁=75 ms

A₁=5 mv

d₂=40 ms

A₂=3 mv

Thus, a point along the ECG wave is determined as a R-peak, if theamplitude increment exceeds amplitude A₁ for period D₁ or amplitude A₂for period D₂. Formula (1) reflects a clearly is defined peak, whileformula (2) reflects a weakly defined peak with small amplitude. Afteran approximate R-peak is identified the point undergoes refinementwithin time period d_(p) (FIG. 9B) Empiric values of d_(p) and A_(d) are200 ms and 1 mv correspondently.

Once these R-peaks have been determined, in step 706 informationanalyzer 230 defines an R—R interval. The R—R interval is defined as thedistance between two consecutive R-peaks. Once the R—R intervals havebeen determined, then in step 708, information analyzer 230 candetermine the amount of noise in the reading of the ECG signal. Thenoise level analysis is performed within the current R—R interval shownin FIG. 8B. If this noise level exceeds predefined limit current R—Rinterval is disregarded. This noise level analysis is essential becauseit excludes from further analysis R—R intervals with artifacts exceedingacceptable level. The noise in ECGs is generated primarily by skeletalmuscle activity. Other sources of noise include physical contact withany of sensors 120 and 122, or a loose-connection between the user'sbody and sensors 120 and 122, interference from outside sources such ascellular telephones, microwaves from microwave ovens, television sets,alarm clocks, or electromotive forces or EMF from electrical appliances,which was not filtered by the Amplifier.

If the noise exceeds acceptable level, R—R interval is excluded fromfurther calculations. A visual indication of the relative noise levelmay be presented to the user. Noise level analysis is performed withincurrent R—R interval using formulas as followed:

Starting with N=0, then:

for each given point j from interval [R_(i−2)+e₁, R_(i)−e₁]:

if |(V_(j)−V_(j−1))|>2^(m) and |(V_(j)−V_(j+1))|>2^(m),

then N=N+2^(m), m=3 . . . 0, jε[R_(i−2)+e₁, R_(i)−e₁]

for each given point j from interval [R_(i−2)+e₂, R_(i)−e₂]

if (V_(j)−V_(j−1))>m and (V_(j)−V_(j+1))>m,

then N=N+m, m=30, 20, jε[R_(i−2)+e₂, R_(i)−e₂]:

where:

e₁, e₂—indentations from threshold points (threshold point are empiricvalues equal 75 ms and 115 ms correspondently);

V_(j)—amplitude in point j;

N—noise level value.

Once the significant R—R intervals have been selected, the data is sentto information analyzer for a pulse metric parameter calculation in step710A and a QRS fragment definition in step 710B. Essentially, steps 710Aand 710B determine the plurality parameters discussed above.

For example, the calculation of the Pulsometry parameters include thecalculation of the pulse rate, premature beats, and atrial fibrillationflutter. The pulse rate is based upon R—R intervals whereby the pulserate is calculated as the average value of an R—R interval of the last 4R—R intervals:${P_{N} = \frac{60 \cdot 1000 \cdot 4}{\sum\limits_{i}{R\quad R\quad i}}},{i = {N - 3}},\ldots \quad,N$

The immediate alteration of the pulse is calculated as:

P _(a) =P _(N) −P _(N−3)

Information analyzer 230 can also determine whether the beats that makeup the pulse rate are premature. Premature beats are determined by thefollowing formula: $\frac{R\quad R}{R\quad R\quad n} \geq 0.7$

Wherein:

R—R—the current R—R interval;

R—R_(n)—the “normal” R—R interval;

The “normal” interval is calculated as the average value of the last 10R—R intervals. The R—R interval is included in the sum if it is notpremature or compensated. In this case, the quantity of theextrasystoles is analyzed for a 10 second period. With this design theR—R intervals are updated using the FIFO buffer.

Information analyzer 230 also determines the Atrialfibrillation-flutter, which is calculated as:

F=(F1+F2)/X(%)

wherein

F1—is the extrasystole component

F2—is a variability component

X—is a dynamically calculated value and is typically approximately equalto thirty.

An extrasystole is essentially a premature contraction of the heart,which results in a momentary cardiac arrhythmia. Component F1 isdetermined by the following formula:

F1=(E/G)* 100,

wherein

E is the number of extrasystoles within G number of previous R—Rintervals;

G is the number of R—R intervals, which are used for the calculation ofthe F parameter.

If F1>50%, then F1 is considered equal 50%. The variability of twoconsecutive R—R intervals is calculated as:

 F _(RR)=(RR _(max) −RR _(min))/RR _(max)* 100

If m1>2 then

F2=S1/m1 wherein,

S1 is the sum of all of the variabilities of the G intervals; or

If m2>2 then

S2 is the sum of all of the variabilities of all G intervals.

If m1≦2 AND m2≦2 then F2=0.

In this case, m1 is the number of R—R intervals (from G intervals),which have a variability of 10% <F_(RR)<30%. In addition, m2 is thenumber of R—R intervals (from G intervals, which have variabilityF_(RR)<10%.

QRS fragment is defined as ECG interval between peaks R_(i−2) and R_(i)(FIG. 8B).

QRS_(i) complex (FIG. 8B) is interval [N₁,N₂] where

N₁=R_(i−1)−Z₁

N₂=R_(i−1)+Z₂

Initial empiric values of Z₁ and Z₂ are 40% and 60% correspondently.Points N₁ and N₂ are refined within further calculations ofcharacteristic points.

FIG. 7B shows the process of step 712, which includes the steps ofdetermining a series of characteristic points of the QRS complex shownin FIG. 8A. In this case, points Q, R, S, J, and P, are known basic or(dominant) characteristic points of the QRS complex shown in FIG. 8A.Points P, I, K, P1, P2, T1, and T2 are defined as auxiliarycharacteristic points. These points are determined or calculated in amanner similar to point R by a waveform analyzer, which is incorporatedinto information analyzer 230. Thus, in step 712A, information analyzer230 calculates points R, Q, S, J, K, I and I_(N).

Point Q is calculated from the graph in FIGS. 10A-10C in the followingmanner:

By following the graph from point R to point R−D_(Q) wherein

A _(i−1) >A ₁ and (A _(R) −A _(i))>A _(RQ)

Where

A—is the amplitude;

A_(RQ) is an empiric value that can equal 2 mv;

D_(Q) is an empiric value equal to 75 ms;

Q is a wave shown in FIG. 10A.

By following the graph from point R to point R−D_(Q) wherein

(A _(i) −A _(i−3))<A _(d) and (A _(R) −A _(i))>A _(RQ) , i=R, . . . R−D_(G)

Where

A—amplitude and empiric values

A_(d)=0.5 mv

A_(RQ)=2 mv

D_(Q)=75 ms;

Q-wave is shown in FIG. 10B.

Approaching point Q from the left and traveling along the graph frompoint R to point R−D_(Q) wherein:

(d1/d2)≧Qr and (A _(r) −A _(i))>A _(RQ)

d1=A_(I)−A_(i−3)

d2=A_(i+3)−A_(i)

Qr=0.45

A_(RQ)=2 mv

D_(Q)=75 ms

Q-wave is shown in FIG. 10C.

Point S is determined by using probability methods whereby travelingalong the graph from point R to point R+D_(s) and approaching point Sthe point is identified if:

A _(i+1) >A ₁ and (A _(R) −A _(i))>A _(RS) wherein i=R, . . . R+D _(S)

Where

A—is the amplitude;

A_(Rs) is an empiric value that can equal 2 mv;

D_(s) is an empiric value equal to 75 ms;

S is a waveform shown in FIG. 11A.

By following the graph from point R to point R+D_(s) and approachingpoint S from the left point S is calculated by a probability formulawherein

(A _(i) −A _(i−3))<A _(d) and (A _(R) −A _(i))>A _(RQ) , i=R, . . . R−D_(s)

Where

A—amplitude and empiric values

A_(d)=0.5 mv

A_(RS)=2 mv

D_(S)=75 ms;

S is a waveform shown in FIG. 11B.

Approaching point S from the left and traveling along the graph frompoint R to point R−D_(Q). The point has been identified if:

(d1/d2)≧Sr and (A _(r) −A _(i))>A _(RS)

where

d1=A_(i)−A_(i−3)

d2=A_(i+3)−A_(i)

Sr=0.3

A_(RS)=2 mv

S is a waveform shown in FIG. 11C.

After identification, points Q and S are refined if they are not definedclearly.

Approaching point J from point S to point S+D_(J) The point has beenidentified if

(A _(i−3) −A _(i))<A _(d) i=S, . . . , S+D _(j),

where

A—amplitude;

A_(d)=0.5 mv;

D_(j)=75 ms;

If point J has not been identified it is considered equal to point S.

Approaching point I from point Q to point Q−D_(I). The point has beenidentified if

(A _(i−3) −A _(i))<A _(d) i=Q, . . . ,Q−D _(I)

where

A—amplitude;

A_(d)=0.5 mv

D_(I)=40 ms

If point I has not been identified, it is considered equal to point Q.

Point K has been defined as (J+D_(j), A_(j+Dj))

where:

D_(j)=80 ms;

A_(j+Dj) is amplitude in point J+D_(j)

Next, in step 712B, after point Q, R, S, I, J, K have been identified,information analyzer 230 filters and smoothes QRS fragment using CubicSpline Interpolation method and sends for visualization as shown in FIG.7A, step 713.

In step 712C, points T, T1, T2, P, P1, P2 are calculated.

Point T is identified using one of probability methods staring from themost reliable. Point T is defined by three geometrical methods dependingon the form of point T.

First, from point J to point N2, the point has been identified if thedistance from point (i, A_(i)) to line (I, I_(N))>T_(Amin),

where:

(i, A_(i))—maximum point

T_(Amin)=2 mv

This method is used for identification of “normal” point T. T waveformis shown in FIG. 12A.

If “normal” point T is not found, inverse point T is identified.

(i, Ai)—is a minimum point;

TAmin=8

T-wave form is shown in FIG. 12B.

Approaching from point J to point N2 point T has been identified if:

(A _(i) −A _(i+5))>A _(d) , i=J, . . . N 2,

where

A—amplitude

A_(d)=5 mv

T-wave from is shown on FIG. 12C.

The system proceeds through these steps whereby the system first triesto find the T wave using the point shown in FIG. 12A; next if the systemis not able to find the T wave it applies a second algorithm whereby itlooks to find it based upon the point shown in FIG. 12B.

If the point is still not identified, the system concludes that point Tdoesn't have clearly defined maximum whereby the third algorithm isapplied as shown in FIG. 12C.

Point T2 (the end of the T-wave)

Approaching T from N2, point T2 has been identified if:

A_(i)>A_(i+1); or if the angle contained by the X axis and the currentpoint becomes less than the previous angle contained, and this tendencycontinues within a 40 ms time period.

The similar method is used to calculate point T2 if T-wave has aninverse shape.

Point T1 (beginning of T-wave)—the similar calculation of T2 pointmethods are used.

Point P has been identified by approaching from point I to N1 whereinpoint P has been identified if:

A _(i) −A _(i−5) >A _(d) and A _(i)−A_(i+5) >A _(d;)

where

A_(d)=2

Point P2 is identified using algorithms similar to the algorithmsapplied to the identification of point T2.

Point P1, which is the beginning of the P wave is defined using theformula:

P 1 =P−(P 2 −P)

Point I_(N) (point I for the next QRS-complex) is identified as point Iof QRS-complex.

Next, in step 712D, the local isoline is calculated. The local isolineis considered a straight line between points L1 and L2. This isoline isusually bounded by point P1 on the first left point or point I, and theright most point can be either point T2 if that point exists, otherwisethat point is I_(N). If the angle of the local isoline exceeds 35° thenthe isoline is considered invalid.

In step 714, after defining all of the characteristic points of theQRS-complex, the QRS complex parameters are calculated. These QRScomplex parameters include the ST-segment depression/elevation (ST_(d));the width of the Q-wave (W_(Q)); amplitude of Q-wave; the width of theQRS complex (W_(QRS)); the width of the PQ interval (W_(PQ)); the widthof the QT interval (W_(QT)); the amplitude of the R wave (A_(R)); the Twave inversion, which is the position of the T wave over the localisoline; and the ratio of the amplitude of the Q-wave (A_(Q)) to theamplitude of the R wave (A_(R))

These elements are calculated through the following formulas: TheST-segment depression elevation or (ST_(d)) is defined as the distancefrom point k to the isoline.

The increase of the Q wave is calculated as:

W _(Q)=((I−Q)*1000)/F(ms)

Wherein F is the frequency of the ECG digitization.

The increase of the Q wave amplitude is defined as Q/R or

A _(QR)=((A _(I) −A _(Q))/(A _(R) −A _(I)))*100%

wherein

A_(I) is the amplitude at point I;

A_(Q) is the amplitude at point Q; and

A_(R) is the amplitude at point R.

The sudden increase of the QRS duration is calculated as

W _(QRS)=((J−I)*1000)/F(ms)

wherein

F=is the frequency of ECG digitization.

In this case the increase of the PQ interval is calculated as:

W _(PQ)=((I−P ₂−2*P)*1000)/F(ms)

wherein

F=is the frequency of ECG digitization.

The increase of the QT interval is calculated as:

W _(QT)=((T ₂ −I)*1000)/F(ms)

wherein

F=is the frequency of the ECG digitization.

The decrease of the R wave amplitude is calculated as:

A _(RD)=(A _(R) −A _(I))*0.2(mv).

Finally the T wave inversion has been defined within the identificationof point T.

In step 716 these parameters are averaged using a number of significantQRS complexes.

Once all of these points have been calculated, in step 718, theseparameters are placed upon the QRS wave so that a user can visualizethese points or parameter points.

Now that the plurality parameters have been determined from the QRSwave, they are compared in real time with a set of stored parameters instorage device 240. FIG. 13 is a flow chart showing an analysis of theplurality of parameters. This flow chart symbolizes the process thatparameter analyzer 250 goes through to determine plurality distinctparameters relating to a person's health. For example, in step 1302, theplurality of parameters are received from information analyzer 230 andin step 1304, these parameters are next stored in storage device 240.Next, in step 1306, parameter analyzer 250 compares the pluralityparameters recently received from information analyzer 230 with a set ofa plurality of preset parameters stored in a database having a table ininformation storage device 240 and shown in FIG. 14. These plurality ofpreset parameters are determined through a series of former users.Essentially, whenever a user experienced an actual medical condition ora medical symptom, these plurality parameters were recorded and used tomake an evaluation of a patient. Over time, the preset values for theseplurality parameters were determined by the average readings for aseries of patients experiencing abnormal medical conditions.

Once the parameters have been compared in step 1306, in step 1308,parameter analyzer 250, a medical professional, or the user, can chooseto update the preset values using an average of that user's values, orreset a new range of preset values. These updated preset values can beplaced either in data storage device 240 or into personal data storagedevice 245. In that way, these preset values can be customized for eachuser. If the preset values are updated, then the original preset valuesare placed in an inactive file in data store 240 while the new presetvalues are placed in an active file in data store 240.

In step 1310 the parameters fed from information analyzer 230 are thenre-compared to this new set of pre-set values so that abnormalityidentifier 260 can determine whether a user is experiencing an abnormalmedical condition.

FIG. 15 is a table for determining whether to trigger an alarm dependingon the comparison of the present time parameters with the presetparameters stored in data store 240.

Essentially, when a reading of a parameter falls between a low value anda high value, on most or all of the parameters, then the user would beconsidered in a safe zone. However, when one or more of the parametershas a reading that falls between a low or very low value or a high orvery high value, then the user may receive an alarm or just a warning.However, if the parameter value falls below a very low value or above avery high value then the user would definitely receive an alarm.Essentially there are at least twenty-two possible permutations betweenabnormal readings of the fifteen parameters and the possible abnormalmedical condition.

For example, in step 1502, abnormality identifier 260 determines theposition for each of the parameter readings, in each range. Next, instep 1506, abnormality identifier 260 determines which alarm to signalfrom each parameter reading. In step 1504, abnormality identifierdetermines which warning to signal from each parameter. In addition,there are a series of warnings or alarms that may be triggered as aresult of extended analysis which includes assessment of complicity oftwo or more abnormal parameters, analysis of certain parameters overextended time period, and overall risk assessment based on the patient'smedical, history, age, weight, and gender. Therefore, in step 1508,abnormality identifier 208 determines whether an extended analysis willsignal an alarm and/or generate an external stimuli. In addition, instep 1510, abnormality identifier 260 determines which warning to signalfrom the extended analysis. Finally in step 1512 this alarm and warninginformation is sent on to alarm controller 280 for further processing.

FIGS. 16A-16F shows a series of tables that show the eighteen warningsand alarms and their corresponding threshold parameters. For example,alarm or warning A1 would signal one of the following possible clinicalabnormalities: sick sinus node syndrome; slow ventricular rhythm;AV—block II-III degree. Alarms or warnings A2 and A3 would signal aparoxysm of tachycardia. Alarm A4 would signal a sudden heart block or(max syndrome) or an AV block II-III degree. Alarm AS would signal asinus arrest or a cardiac arrest. Alarm A6 would signal Extrasystoles.Alarm A7 would signal group Extrasystoles. Alarm A8 would signalparoxysm of atrial fibrillation flutter.

Alarms A9 and A10 would signal myocardial ischemia, while alarm Allwould also signal myocardial ischemia as well as myocardial infarctionand bundle branch blocks. Alarms A12 A13 and A14 would signal myocardialinfarction, and bundle branch blocks. Alarms A15, and A16 would signal ahigh risk of ventricular tachyarrhythmias. Alarm A17 would signal bundlebranch blocks, while Alarm A18 would signal an AV-block.

Alarms GE_(A7), ST_(W9), ST_(A9), ST_(W10), ST_(A10), W_(G), and A_(G)would signal cardiac events and prognoses resultant of extendedanalysis.

This model can be used to predict future problems as well. For example,FIG. 17 shows the process for determining whether to send a warning to auser based upon readings from the parameters and the user's medicalhistory. In step 1702, the parameter reading is determined wherein ifthis reading falls within a warning range, the system proceeds to step1704 whereby the user's medical history is analyzed. Thus, if the useris overweight with high blood pressure and has had a history of heartattacks, an abnormal parameter reading would increase that user's riskfor an abnormal medical condition such as a myocardial infarction.

Thus, in step 1706 the risk of such a condition is analyzed using thehistory of that user as well as a history of conditions for a number ofpast users. In this case, the accuracy of this step will continue toimprove as the user continues to wear portable information reader 100.The preset values for that user could be continuously updated andcompared to a history of values for that user as well as other usersthat have been tested.

Next, in step 1708 the risk profile of the user is analyzed. The riskprofile is based upon empirically derived data and does not require anyspecific input from the user. The risk profile is essentially asensitivity setting for the alarm. Thus, if the alarm is set to behighly sensitive, then a warning may sound at the slightest abnormalityin parameter readings. Finally, if the criteria have been met, then instep 1710 the alarm is sounding sending a warning to that user.

To determine a warning range for a user, step 1702, which includes thestep of determining a parameter reading, which includes determining theRR reading for a user. This determination includes having parameteranalyzer 250 use the following formula to determine whether a user isentering a dangerous range:${R\quad R} = {1 + \sqrt[ - ]{\left. {1.49*} \middle| \frac{{STmeas} - {STinit}}{{STinit}.{+ {{STthresh}.}}} \middle| {}_{2}{+ \left| {\frac{{QTmeas}.}{{QTnorm}.} - 1} \middle| {}_{2}{+ \left| \frac{N_{1} + {34.91*N_{2}} + {73.68*N_{3}}}{H\quad R} \right|^{2}} \right.} \right.}}$

Wherein:

RR—is the complex Relative Risk of sudden cardiac death and developmentof myocardial infarction.

HR is the heart rate per minute (BPM);

ST init.—ST segment level before observation beginning;

ST meas.—ST segment level at the current moment;

ST thresh.—ST segment threshold normal levels;

QT meas.—QT interval duration at the current moment;

QT norm.—QT interval normal duration determined with the Basset Formula:

QTnorm=k*{square root over (60/HR)}

k is a constant coefficient of 0.4 for males and 0.37 for females;

N1 is the amount of single ventricular extrasystoles per min;

N2 is the amount of coupled ventricular extrasystoles per min;

N3 is the amount of ventricular tachycardia runs (more than two in arow) per min.

Based upon the RR parameter defined above, the value of the suddencardiac death and myocardial infarction risk assessment is made. Whereinthe higher the number reading for RR the higher the likelihood of riskfor medical complications.

Essentially, in this step, the remote medical device couples with acomputer to perform the associated method to calculate multipleparameters to predict when a patient will experience a medicalabnormality listed above.

The system as shown in FIG. 18 uses three level adaptively to adjust therange of threshold parameters. The three levels are:

Level 1: The baseline (normal) threshold levels which are preset in thesystem which are well known to practitioners will be adjustedconsidering age, gender, weight, and/or medical history.

Level 2: The system analyzes the log file of cardiac events and patienthistorical data, which are used to determine new range of parameters.These new parameters should be confirmed by medical personnel andentered into the system manually. One or more of the parameters may bethe same as the baseline parameters.

Level 3: The system may update some of the parameters automatically.These changes are limited to pre-selected parameters within selectedranges. If the system recommends a change beyond the specified rangethen it becomes a level 2 change.

This device and forecasting method was used to test ten patients forsudden cardiac death and myocardial infarction Risk forecasting. Thesepatients are as follows:

Patient No. 1

Male Born 1945 clinical diagnosis: coronary artery disease, stableangina, and frequent ventricular extrasystoles.

Monitoring Observation Data:

ST segment depression (ST meas.): 3 mv

QT interval duration (QT meas.): 600 ms,

The amount of single ventricular extrasystoles (N1) per min 15,

Coupled extrasystoles (N2)—3

Sequential ventricular extrasystoles (N3) consisting of threecomplexes—1 episode,

Heart rate 85 BPM.

RR: 5.5

Wherein RR is the sudden cardiac death and myocardial infarctiondevelopment complex risk (RR) computed with this applicant forecastingmethod.

The unfavorable prognosis was proven, in two days the patient sufferedfrom acute coronary syndrome and ventricular fibrillation. The patientwas successfully resuscitated and adequate anti-ischemic andanti-arrhythmic therapies were assigned.

Patient No. 2

Male born 1937. Clinical diagnosis: coronary artery disease, stableangina.

Monitoring Observation Data:

ST segment depression (ST meas): 2 mv

QT interval duration (QT meas): 400 ms

Amount of single ventricular extrasystoles (N₁) per min: 9

Coupled ventricular extrasystoles (N₂): 1

Sequential ventricular extrasystoles (N₃): 0

Heart Rate: 72 BPM

RR: 3.5

The unfavorable prognosis was proven, wherein in one month the patienthad suffered from development of a complex myocardial infarction.

Patient No. 3

Male born 1947. Clinical diagnosis: Arterial hypertension, frequentventricular extrasystoles.

Monitoring Observation Data:

ST segment depression (ST meas): 0 mv

QT interval duration (QT meas): 500 ms

Amount of single ventricular extrasystoles (N₁) per min: 12

Coupled ventricular extrasystoles (N₂): 2

Sequential ventricular extrasystoles (N₃): 2

Heart Rate: 78 bpm

RR: 4.0

Unfavorable prognosis was proven, in seven days the patient has sufferedfrom cardiac arrest resulting from ventricular fibrillation. Patientdied despite having resuscitation methods performed on him.

Patient No. 4

Male born 1937 clinical diagnosis: coronary artery disease, stableangina.

Monitoring Observation Data:

ST segment depression (ST meas): 1 mv

QT interval duration (QT meas): 400 ms

Amount of single ventricular extrasystoles (N₁) per min: 0

Coupled ventricular extrasystoles (N₂): 0

Sequential ventricular extrasystoles (N₃): 0

Heart Rate: 66 bpm

RR: 2.2

The patient was under observation for 3 months, there were nounfavorable episodes.

Patient No. 5

Male born 1945. Clinical diagnosis: arterial hypertension.

Monitoring Observation Data:

ST segment depression (ST meas): 0 mv

QT interval duration (QT meas): 360 ms

Amount of single ventricular extrasystoles (N₁) per min: 0

Coupled ventricular extrasystoles (N₂): 0

Sequential ventricular extrasystoles (N₃): 0

Heart Rate: 68 bpm

RR: 1.0

The patient was under observation for three months, there were nounfavorable episodes.

Patient No. 6

Male born 1944, clinical diagnosis: coronary artery disease stableangina.

Monitoring Observation Data:

ST segment depression (ST meas): 3 mv

QT interval duration (QT meas): 370 ms

Amount of single ventricular extrasystoles (N₁) per min: 0

Coupled ventricular extrasystoles (N₂): 0

Sequential ventricular extrasystoles (N₃): 0

Heart Rate: 82 bpm

RR: 4.7

An unfavorable prognosis was proven. In 4 days the patient has sufferedfrom the development of acute myocardial infarction complicated bypulmonary edema. The patient was repeatedly hospitalized and died in 7hours.

Patient No. 7.

Male born 1960. Clinical diagnosis post myocardial sclerosis.

Monitoring Observation Data:

ST segment depression (ST meas): 0 mv

QT interval duration (QT meas): 600 ms

Amount of single ventricular extrasystoles (N₁) per min: 0

Coupled ventricular extrasystoles (N₂): 0

Sequential ventricular extrasystoles (N₃): 0

Heart Rate: 60 bpm

RR: 1.5

The patient was under observation for 2 months, there were nounfavorable episodes.

Patient No. 8

Male born 1960 clinical diagnosis: arterial hypertension.

Monitoring Observation Data:

ST segment depression (ST meas): 0 mv

QT interval duration (QT meas): 400 ms

Amount of single ventricular extrasystoles (N₁) per min: 19

Coupled ventricular extrasystoles (N₂): 2

Sequential ventricular extrasystoles (N₃): 1

Heart Rate: 66 bpm

RR: 3.5

In 45 minutes the patient suffered from developed stable ventriculartachycardia, which was stopped with defibrillation.

Patient No. 9.

Male born 1944. Clinical diagnosis: coronary artery disease, myocardialinfarction, and unstable angina.

Monitoring Observation Data:

ST segment depression (ST meas): 3 mv

QT interval duration (QT meas): 550 ms

Amount of single ventricular extrasystoles (N₁) per min: 18

Coupled ventricular extrasystoles (N₂): 2

Sequential ventricular extrasystoles (N₃): 1

Heart Rate: 82 bpm

RR: 5.3

Unfavorable forecast was proven. In one hour the patient has sufferedfrom the development of acute myocardial infarction. The patient wasrepeatedly hospitalized and died in one hour.

Patient No. 10

Male born 1955. Clinical diagnosis: coronary artery disease, myocardialinfarction, and unstable angina.

Monitoring Observation Data:

ST segment depression (ST meas): 3 mv

QT interval duration (QT meas): 580 ms

Amount of single ventricular extrasystoles (N₁) per min: 14

Coupled ventricular extrasystoles (N₂): 2

Sequential ventricular extrasystoles (N₃): 2

Heart Rate: 80 bpm

RR: 5.8

An unfavorable forecast was proven, in 8 hours the patient has sufferedfrom the development of ventricular fibrillation.

The patient was repeatedly hospitalized and successfully defibrillated.

Essentially this formula for determining RR as stated above can becontinually modified so that it becomes more accurate in general andmore accurate for each user. Essentially as more patients are analyzedthe formula will read as:${R\quad R} = {1 + \sqrt[ - ]{\left. {K_{1}*} \middle| \frac{{STmeas} - {STinit}}{{STinit}.{+ {{STthresh}.}}} \middle| {}_{2}{+ \left| {\frac{{QTmeas}.}{{QTnorm}.} - 1} \middle| {}_{2}{+ \left| \frac{N_{1} + {K_{2}*N_{2}} + {K_{3}*N_{3}}}{H\quad R} \right|^{2}} \right.} \right.}}$

Wherein the variables are the same as above except:

K₁ is a first constant, which was originally 1.49;

K₂ is a second constant, which was originally 34.91;

K₃ is a third constant, which was originally 73.66.

Constants K₁, K₂ and K₃ can be varied depending upon the clinical dataobtained. As more experiments and trials are performed, the constantsmay be modified to provide more accurate forecasting.

Once a user starts to have problems, the system can then locate the userso that health care professionals can contact that user. FIG. 19 is aflowchart showing the process for locating a user once the alarm hasbeen triggered. In step 1902 the alarm sends a signal to the GPS unit.In step 1904 the GPS unit turns on and then initializes by sending asignal of the coordinates of the user to remote storage and processingdevice 300 to locate the user in step 1906. If the GPS system is coupledto remote device 100 then the signal is first relayed to informationprocessing device 200. In step 1908 the user's location is defined.Next, the coordinates of that user are transmitted to central station300 so that a medical professional can contact a user in person.

Thus, when the user wants to monitor his or her vital signs, that userplaces a belt containing sensors 120 and 122 on its body. Sensors 120and 122 read information from that user including information about theuser's pulse and heart rate. This information is then sent ontoamplifier 130 whereby the signal is amplified before it is fed throughan analog to digital (A/D) converter 140. Analog to digital converter140 sends this information onto MICRO controller 150. MICRO controller150 transforms this information into blocks of information fortransmission by transceiver 160. Transceiver 160 sends this informationonto transceiver 210, which receives this information and sends it ontomedical information analyzer 230.

Medical information analyzer 230 works along with data store 240 andparameter analyzer 250 to compare this data with a preset amount ofdata. Parameter analyzer 250 next sends this information ontoabnormality identifier 260, which identifies whether any of the signalsare abnormal. Next, alarm controller 280 sends this information onto theuser as well as a base station housing information storage device 300through transceiver 284.

One of the major advantages of this invention is that this designmonitors the user in a continuous real time fashion allowing acompletely free movement of the user.

In addition, another advantage of this invention is that through aseries of preset values for a set of plurality parameters, the systemcan determine whether a user has entered or will enter into an abnormalmedical condition such as a heart attack. This set of pre-set values canbe continuously updated so that it becomes increasingly more accuratefor each user.

Still another advantage of the system is that the system utilized thethree level process for determining whether a user will have a futureoccurrence of an abnormal medical condition. These three levels are:

Instantaneous detection of developing cardiac events;

Evaluation of the complicity of number of minor cardiac abnormalitiesleading to development of significant cardiac events;

Risk assessment of development Myocardial Infarction and Sudden CardiacDeath.

Accordingly, while several embodiments of the present invention havebeen shown and described, it is to be understood that many changes andmodifications may be made thereunto without departing from the spiritand scope of the invention as defined in the appended claims.

What is claimed is:
 1. A process for analyzing a medical condition of auser comprising the following steps: a) reading at least one signal fromthe user; b) transforming said at least one signal into at least onedigital signal; c) extracting a plurality of parameters from said atleast one digital signal, wherein said step of extracting a plurality ofparameters comprises the following steps: extracting at least twoconsecutive R-peaks from said digital signal; analyzing noise in saiddigital signal between said at least two consecutive R-peaks found fromsaid step of extracting R-peaks; determining at least one significantR—R interval; determining a plurality of pulse metric parameters; anddetermining a plurality of characteristic points of a QRS complex; d)analyzing said plurality of parameters and comparing said plurality ofparameters to a range in a set of a plurality of preset parameters; e)determining whether a user has an abnormal medical condition bydetermining whether said plurality of parameters fall outside of saidrange in said plurality of parameters; and f) determining whether totrigger an alarm warning the user of his or her medical condition whensaid at least one of said plurality of parameters fall outside of saidpreset ranges for said preset parameters.
 2. The process as in claim 1,further comprising the step of selecting a particular alarm from a setof a plurality of alarms for the user based upon which of said pluralityof parameters fall outside of said range of said plurality of saidpreset parameters.
 3. The process as in claim 1, wherein said step ofextracting a plurality of parameters includes extracting a plurality ofdata points from said at least one digital signal and forming at leastone QRS wave from said plurality of data points.
 4. The process as inclaim 1, wherein said step of determining whether a user has an abnormalmedical condition includes the step of setting said range in saidplurality of preset parameters based upon an average formed from aplurality of previous users.
 5. The process as in claim 1, wherein saidstep of determining a plurality of pulse metric parameters includesdetermining: a pulse rate; a plurality of premature beats; and an atrialfibrillation/flutter.
 6. The process as in claim 5, wherein said step ofextracting at least two consecutive R-peaks from said digital signalincludes using the following formula: (V−V1)>A1 or (V−V2)>A2 wherein Vis the amplitude at a current point along the QRS fragment; V1 is theamplitude at (t−d1) V2 is the amplitude at point (t−d2) wherein t is thecurrent time and d1, d2, A1, and A2 are empiric constants.
 7. Theprocess as in claim 5, wherein said step of determining a pulse rate iscalculated using a running average value of a plurality of R—Rintervals.
 8. The process as in claim 7, wherein said step ofdetermining a pulse rate includes using a running average value of fourR—R intervals.
 9. The process as in claim 5, wherein said step ofdetermining premature beats includes using the following formula:RR/RRn≦0.7 whereby RR is the current R—R interval; and RRn is the“normal” R—R interval.
 10. The process as in claim 5, wherein said stepof determining said atrial fibrillation/flutter includes using thefollowing formula: F=(F1+F2)/X% wherein F1 is an extrasystole component;F2 is a variability component; X is a dynamically calculated value. 11.The process as in claim 1, wherein said step of determining a pluralityof characteristic points of a QRS complex includes the steps of:determining a plurality of dominant characteristic points; determining aplurality of auxiliary characteristic points; and determining pluralityof QRS complex parameters.
 12. The process as in claim 11, wherein saidstep of determining a plurality of dominant characteristic pointsincludes determining points Q, R, S, J, P and T on a QRS complex. 13.The process as in claim 11, wherein said step of determining a pluralityof auxiliary characteristic points includes determining points I, K, P1,P2, T1, and T2.
 14. The process as in claim 11, wherein said step ofdetermining a plurality of QRS complex parameters includes: calculatinga ST-segment depression/elevation; calculating a width of a Q-wave (WQ);calculating an amplitude of the Q-wave; calculating a width of QRScomplex; calculating a width of PQ interval; calculating a width of QTinterval; calculating an amplitude of R wave; calculating a T waveinversion; and calculating a ratio of amplitude of Q wave to amplitudeof R wave.
 15. The process as in claim 1, further comprising a step ofsmoothing ECG waves by use of a cubic spline approximation.
 16. Theprocess as in claim 1, wherein said step of determining an R—R intervalinvolves determination of at least 2 consecutive significant R-peaks.17. The process as in claim 1, wherein said step of analyzing noiseinvolves filtering out bioelectrical signals generated by skeletalmuscle activity and excluding R—R intervals with noise level N exceedingpredefined level, wherein noise level N may include a visualrepresentation as follows: believing N=0, then: for each given point jfrom interval [R¹⁻²+e_(l), R₁−e₁]: if |(V_(j)−V_(j−1))>2^(m) and|(V_(j)−V_(j+1))>2^(m), then N=N+2^(m), m=5, . . . , jε[R¹⁻²+e₁,R_(i)−e₁] for each given point j from interval [R¹⁻²+e₂, R₁−e₂]: if(V_(j)−V_(j−1))>2^(m) and (V_(j)−V_(j+1))>2^(m), then N=N+2^(m), m=5 . .. , 2 jε[R¹⁻²+e₂, R₁−e₂] where: e₁, e₂—indentations from thresholdpoints (threshold point are empiric values equal 75 ms and 115 mscorrespondently); V_(j)—amplitude in point j; N—noise level value. 18.The process as in claim 1, further comprising the step of determiningwhether to send a signal to the user to administer a form of externalstimuli.
 19. A process for analyzing a medical condition of a usercomprising the following steps: reading at least one signal from theuser; transferring said at: least one signal into at least one digitalsignal; extracting a plurality of parameters from said at least onedigital signal; analyzing said plurality of parameters and comparingsaid plurality of parameters to a range in a set of preset parameters;predicting a possibility of a future occurrence of an abnormal medicalcondition in the user using at least one of said plurality ofparameters, wherein said step of predicting a possibility of a fixtureoccurrence of an abnormal medical condition includes using at least oneof the following parameters: Heart Rate per minute (HR) of the user; STinitial which is the ST segment level before observation beginning, STmeasure which is the ST segment level at the current moment, STthreshold—which is the ST segment threshold at normal levels, QTmeasure—which is the QT interval duration at the current moment; QTnormal which is the QT interval normal duration, wherein said step ofpredicting a possibility of a fixture occurrence of an abnormal medicalcondition includes using the following formula:${R\quad R} = {1 + \sqrt[ - ]{\left. {K_{1}*} \middle| \frac{{STmeas} - {STinit}}{{STinit}.{+ {{STthresh}.}}} \middle| {}_{2}{+ \left| {\frac{{QTmeas}.}{{QTnorm}.} - 1} \middle| {}_{2}{+ \left| \frac{N_{1} + {K_{2}*N_{2}} + {K_{3}*N_{3}}}{H\quad R} \right|^{2}} \right.} \right.}}$

wherein K₁ is a first constant; K₂ is a second constant; K₃ is a thirdconstant; N₁ is a number of single premature beats per minute; N₂ is anumber of group premature beats per minute; N₃ is a number offibrillation/flutter episodes per minute; and determining whether a userhas an abnormal medical condition by determining whether said pluralityof parameters fall outside of said range in said plurality of parametersand determining whether to trigger an alarm warning the user of his orher medical condition or possible future medical condition when said atleast one of said plurality of parameters fall outside of said presetranges for said preset parameters.
 20. The process as in claim 19,wherein said abnormal medical condition is a development of myocardialinfarction or sudden cardiac death.
 21. The process as in claim 19,further comprising the step of adjusting constants K₁, K₂ and K₃,depending upon a set of clinical data obtained by predicting saidabnormal medical condition, so that as more experiments and trials areperforated, said constants may be modified to provide more accurateforecasting.
 22. The process as in claim 19, wherein initial values ofsaid constants: k₁ is approximately 1.49, K₂ is approximately 34.91, andk₃ is approximately 73.68.
 23. The process as in claim 19, furthercomprising the step of setting a range for said at least one presetparameter to predict said future occurrence of said abnormal medicalcondition.
 24. The process as in claim 23, further comprising the stepof adjusting said range for said at least one preset parameter using atleast one level of adaptability.
 25. The process as in claim 23, furthercomprising the step of adjusting said range for said at least one presetparameter using a first level, a second level and a third level ofadaptability.
 26. The process as in claim 25, wherein said second levelincludes adjusting said range based upon a log file of cardiac eventswherein said adjustment is actuated by a person controlling the settingof the range.
 27. The process as in claim 25, wherein said third levelincludes automatically adjusting said range.
 28. The process as in claim19, further comprising the steps of inputting the user's medical historyinto a database and storing said user's medical history.
 29. The processas in claim 28, wherein said first level of adaptability includesadjusting said range based upon at least one of the following user'scharacteristics: age, gender, weight, or medical history.